Sensor based Battery Management System in Electric Vehicle using IoT with Optimized Routing

IoT based BMS (battery management system) is becoming an essential factor of an EV (electric vehicle) in recent years. The BMS is responsible for monitoring and controlling the state of the battery pack in an EV using appropriate. The IoT based BMS continuously monitors the voltage, temperature, and current of each battery cell and adjusts the charging and discharging of the battery pack accordingly from remote areas. The integration of IoT with a BMS can allow isolated monitoring of the EV's battery pack, enabling fleet managers or EV owners to track the performance of their vehicles and monitor their battery health. This paper primarily focus on IoT-Optimized Battery Management System (IoT-OBMS), which comprises two modules, IoT and charging, for effective energy storage management in electric vehicles. With particle filtering, the SOC of the battery in an EV is calculated, along with an estimate of the temperature inside the cell, and the cell parameters are directly estimated to the SOC. The data is stored in the Software Defined Network (SDN), and the Spider Swarm Monkey Optimization (SSMO) algorithm is used to estimate the optimal routing path. In the proposed charging module, the Mamdani fuzzy system rules are implemented with a Boltzmann neural network for BMS. The integration of fuzzy rules in the deep learning model controls and manages the EV battery. Therefore, simulation results illustrate the reasonable value of the proposed system in terms of SOC estimation accuracy, charging time, and cost.